import torch
import torch.nn as nn

class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),  # 输出: 96x55x55
            nn.ReLU(inplace=True),
            nn.LocalResponseNorm(size=5, alpha=1e-4, beta=0.75, k=2.0),
            nn.MaxPool2d(kernel_size=3, stride=2),  # 输出: 96x27x27

            nn.Conv2d(96, 256, kernel_size=5, padding=2),  # 输出: 256x27x27
            nn.ReLU(inplace=True),
            nn.LocalResponseNorm(size=5, alpha=1e-4, beta=0.75, k=2.0),
            nn.MaxPool2d(kernel_size=3, stride=2),  # 输出: 256x13x13

            nn.Conv2d(256, 384, kernel_size=3, padding=1),  # 输出: 384x13x13
            nn.ReLU(inplace=True),

            nn.Conv2d(384, 384, kernel_size=3, padding=1),  # 输出: 384x13x13
            nn.ReLU(inplace=True),

            nn.Conv2d(384, 256, kernel_size=3, padding=1),  # 输出: 256x13x13
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2)  # 输出: 256x6x6
        )
        self.classifier = nn.Sequential(
            nn.Dropout(p=0.5),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),

            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),

            nn.Linear(4096, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x